Sequential change-point detection based on direct density-ratio estimation

Yoshinobu Kawahara, Masashi Sugiyama

Research output: Contribution to journalArticlepeer-review

128 Citations (Scopus)


Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid nonparametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real-world datasets.

Original languageEnglish
Pages (from-to)114-127
Number of pages14
JournalStatistical Analysis and Data Mining
Issue number2
Publication statusPublished - Apr 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analysis
  • Information Systems
  • Computer Science Applications


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